//Transform Healthcare Data into Useful Insights
Healthcare Data Warehouse Service
Leverage our full range of data warehousing services and utilize data warehousing as a profitable tool to attract new customers, build innovative products, and lower healthcare costs. Our specialization in providing strong healthcare data management solutions enables us to develop, maintain, and support healthcare data warehousing and informatics tools.
What is a Healthcare Data warehouse?
It is a centralized repository for electronic health records and clinical data that has been gathered from various sources, processed, examined, and distributed to enable data-driven decisions in a matter of minutes. They assist in converting organizational data into easily accessible, useful information that promotes quality advancement in clinical outcomes, patient experiences, and operational effectiveness. We as your experienced data analytics company can assist you in developing data and metrics as valuable assets to improve standardization and transparency.
The architecture of Healthcare Data Warehouse Solutions
ReapMind develops enterprise data warehouses, which serve as a key component of a healthcare BI solution that includes the following components:
Data Source Layer
Data source layer contains healthcare data from internal and external data sources (ERP, EHR/EMR, CRM, claims management systems, pharmacy management systems, and so on).
Staging Area
Intermediate temporary storage where healthcare data is extracted, transformed, and loaded (ETL) or extracted, loaded, and transform (ELT).
Data storage layer
consists of centralized structured storage. It may also include data marts, which are subsets of healthcare DWH oriented to a specific business line (HR, accounting, etc.) or department (radiology, intensive care, pediatrics, etc.).
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Analytics and BI
are tools for business analytics, data mining, data reporting, and visualization.
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Important features to Look for in a Healthcare DWH
ReapMind offers medical data warehouse solutions, each of which has a different functionality depending on the customer. We've listed the features below that healthcare organizations we work with frequently ask for:
Data Integration
- Taking in structured, semi-structured, and unstructured medical data (from EHR systems, ERP, HR management systems, public medical databases, claims management systems, etc.).
- Healthcare data integration using ETL/ELT.
- Extraction and loading of complete and incremental healthcare data.
- Loading and management of healthcare data.
- Various levels of complexity in healthcare data transformation (data type conversion, summarization, etc.).
- SQL data loading and querying for healthcare.
- Big data ingestion
- Ingestion of streaming data.
Data storage
- Healthcare data storage that is integrated, historical, summarised, and subject-oriented.
- PHI (Protected Health Information) storage.
- Storage of Protected Health Information (PHI).
- Metadata storage
- Healthcare data storage environment alternatives (cloud, on-premises, hybrid).
Database efficiency and reliability
- Elastic scaling of computing and storage resources
- High-performance query processing due to healthcare data indexing, materialized view support, result caching, and ML capabilities to dynamically manage performance and concurrency.
- Automated data backup across various cloud zones and regions for fault tolerance and disaster recovery.
Security and Compliance
- Granular row and column level security control.
- multi-factor authentication
- Encryption of healthcare data in transit and at rest (including backups and network connections).
- Dynamic healthcare data masking.
- Continuous assessment of vulnerabilities and threat.
- Adherence to healthcare regulations (HIPAA, FDA, HITECH, etc.).
Health Data Warehouse Solutions: Beneficial Integrations
Data Lake
While an enterprise DWH for healthcare can store highly structured data, semi-structured and unstructured data (manual patient records, image-based test reports, practitioner’s notes, etc.) can be stored more affordably in a data lake.
The data that is stored in the data lake is used to create the machine learning models, such as forecasting healthcare demand.
Business Intelligence Applications
Healthcare organizations can use a self-service BI system to visualize, analyze, and report the medical data arranged in the EDW in a flexible and independent manner. This makes it possible for key decision-makers to quickly and simply receive analytics insights.
Factors Influencing the Success of Medical Data Warehousing
Based on years of experience in designing and implementing data warehousing solutions, ReapMind has identified a set of factors that, when taken into account, help maximize ROI for DWH projects
150+ Successful Healthcare Projects delivered
200+ tech professionals
Scalability and flexibility of healthcare DWH
In order to effectively address new data analytics goals, it is possible to instantly upload any kind (structured, semi-structured, unstructured) and volume of healthcare-related data.
Healthcare data protection and security measures
Following are some best practices that ReapMind highlights:
- Sensitive patient data should be processed and stored in highly secure environments (AWS, Microsoft Azure, Google Cloud or private servers).
- Make sure to use dynamic data masking, multi-factor user authentication, and constant data encryption.
- Carry out penetration testing and vulnerability analysis for DWH in healthcare, etc.
Long-standing data quality management
ReapMind empowers conducting an extensive analysis of the data warehouse system and developing strong data governance practices to ensure the high quality of the data delivered from a variety of data sources. Various encoding formats, attribute measurements from various data source systems, conflicting key fields, etc. are examples of common data quality challenges that this will aid in addressing.
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Our Data Warehouse Implementation Roadmap
Our data warehouse implementation process can be divided into four steps.
- Planning: The planning stage is essential in the entire data warehouse development process because in this stage we define all future efforts. Our experts deal with assessing the context and thinking about the strategic aspects of adopting a clinical data warehouse.
- Design: At the design stage, we create the architecture of a future data warehouse, define data integration procedures, consider the healthcare data warehouse model, and plan for the necessary integrations.
- Development and implementation: The development stage entails installing the necessary infrastructure components as well as developing and deploying data warehouse software and end-user applications.
- Ongoing and post-migration testing: Additional validation is required after migration in addition to ongoing testing activities that go along with development activities. To verify the migrated data for duplications, errors, contradictions, or inaccuracies, a series of checks are performed.
Why Choose ReapMind to Implement a Health Data Warehouse Solution?
ReapMind has assisted several healthcare organizations in expanding and revolutionizing their care-delivery systems through end-to-end data warehousing services. We'll work with you to implement a reliable EDW that drives informed decisions and profitable actions in the long run because data accuracy is critical to running any healthcare facility efficiently.
We offer our clients a healthcare data warehouse that includes:
A technically sound and secure infrastructure
Leading-edge data analysis tools
The ability to integrate all types of healthcare data for SQL querying.
Ongoing support from data science experts
· Storage and computing resource scalability
EMR, EHR, HIE, and CDS systems are seamlessly integrated.
Cleaning and migrating healthcare data